1. bookVolumen 45 (2020): Edición 4 (December 2020)
Detalles de la revista
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Formato
Revista
eISSN
2300-3405
Primera edición
24 Oct 2012
Calendario de la edición
4 veces al año
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Inglés
access type Acceso abierto

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Publicado en línea: 16 Dec 2020
Volumen & Edición: Volumen 45 (2020) - Edición 4 (December 2020)
Páginas: 257 - 280
Recibido: 30 May 2020
Aceptado: 12 Nov 2020
Detalles de la revista
License
Formato
Revista
eISSN
2300-3405
Primera edición
24 Oct 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés
Abstract

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations.

We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization.

The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).

Keywords

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